
import pandas as pd
import plotly.express as px
import numpy as np

import plotly.io as pio

# This ensures Plotly output works in multiple places:
# plotly_mimetype: VS Code notebook UI
# notebook: "Jupyter: Export to HTML" command in VS Code
# See https://plotly.com/python/renderers/#multiple-renderers
pio.renderers.default = "plotly_mimetype+notebook"

df = pd.read_csv("./satellites.csv", sep=";")

df = df.rename(columns={
    "Perigee (km)": 'perigee',
    "Apogee (km)": "apogee",
    "Current Official Name of Satellite": "name",
    "Name of Satellite, Alternate Names": "name2",
    "Country of Operator/Owner": "country",
    "Operator/Owner": "company",
    "Class of Orbit": "orbit_class",
    "Type of Orbit": "orbit_type"
})

df["perigee"] =  df["perigee"].str.replace(" ", '').astype("float")
df["apogee"] =  df["apogee"].str.replace(" ", '').astype("float")


df = df[["name", "name2", "country", "company", "orbit_class", "orbit_type", "perigee", "apogee"]]
df.columns

Index(['name', 'name2', 'country', 'company', 'orbit_class', 'orbit_type',
       'perigee', 'apogee'],
      dtype='object')


def variancestats(df, var):
    d = dict()
    d["mean"] = df[var].mean()
    d["std"] = df[var].std()
    d["q25th"] = df[var].quantile(0.25)
    d["q75th"] = df[var].quantile(0.75)

    return d

def zscore(df, var):
    r = variancestats(df, var)
    mean = r["mean"]
    std = r["std"]

    zscorevar = "{}_zscore".format(var)
    outliervar = "{}_zscore_outlier".format(var)

    df[zscorevar] = (df[var] - mean) / std
    df[outliervar] = 0
    df.loc[np.abs(df[zscorevar]) >= 2, outliervar] = 1

    return df


def zscore_analysis(df, var):
    dt = df.copy()
    dt = zscore(dt, var)

    zscorevar = "{}_zscore".format(var)
    outliervar = "{}_zscore_outlier".format(var)

    dt = dt.groupby([var, zscorevar, outliervar]).agg({
        "name": "count"
    })

    dt.reset_index(inplace=True)

    fig = px.scatter(dt, x=var, y="name", color=zscorevar)
    fig.show()

zscore_analysis(df, "perigee")
zscore_analysis(df, "apogee")


dq = zscore(df, "perigee")
dt = dq[dq["perigee_zscore_outlier"] == 1]
dt = dt.sort_values(by=["perigee"], ascending=False)

dt.head()


stats = variancestats(dt, "perigee")
stats

{'mean': 35806.03380782918,
 'std': 1302.837668300367,
 'q25th': 35768.0,
 'q75th': 35782.0}


dq = dt[dt["perigee"] > stats["q75th"]]
dq.count()

name                      131
name2                     131
country                   131
company                   131
orbit_class               131
orbit_type                  2
perigee                   131
apogee                    131
perigee_zscore            131
perigee_zscore_outlier    131
dtype: int64


fig = px.scatter(df, x="apogee", y="perigee", trendline="ols")
fig.show()

	name	name2	country	company	orbit_class	orbit_type	perigee	apogee	perigee_zscore	perigee_zscore_outlier
1161	Interstellar Boundary Explorer	Interstellar Boundary EXplorer (IBEX)	USA	National Aeronautics and Space Administration ...	Elliptical	Deep Highly Eccentric	62200.0	268679.0	5.235524	1
866	Geomagnetic Tail Laboratory (Geotail)	Geotail (Geomagnetic Tail Laboratory)	Multinational	Institute of Space and Astronautical Science (...	Elliptical	Deep Highly Eccentric	49551.0	191451.0	4.083549	1
987	GSAT-29	GSAT-29	India	Indian Space Research Organization (ISRO)	GEO	NaN	37782.0	37807.0	3.011718	1
5131	TianLian 2	TianLian 2 (TL-1-02, CTDRS)	China	China Aerospace Science and Technology Corp. (...	GEO	NaN	37778.0	37794.0	3.011353	1
5024	STPSat-6	STPSat-6	USA	Atlas 5	GEO	NaN	36097.0	36110.0	2.858260	1

Look for outliers¶

Relations¶